Databricks-Certified-Data-Engineer-Associate Practice Exam Questions with Answers Databricks Certified Data Engineer Associate Exam Certification

To minimize the total running time of the SQL endpoint used in the refresh schedule of a dashboard in Databricks, the most effective approach is to utilize the Auto Stop feature. This feature allows the SQL endpoint to automatically stop after a period of inactivity, ensuring that it only runs when necessary, such as during the dashboard refresh or when actively queried. This minimizes resource usage and associated costs by ensuring the SQL endpoint is not running idle outside of these operations.

A webhook alert destination is a way to send notifications to external applications or services via HTTP requests. A data engineer can use a webhook alert destination to notify their entire team via a messaging webhook, such as Slack or Microsoft Teams, whenever the number of NULL values in the input data reaches 100. To set up a webhook alert destination, the data engineer needs to do the following steps:

In the Databricks SQL workspace, navigate to the Settings gear icon and select SQL Admin Console.

Click Alert Destinations and click Add New Alert Destination.

Select Webhook and enter the webhook URL and the optional custom template for the notification message.

Click Create to save the webhook alert destination.

In the Databricks SQL editor, create or open the query that returns the number of input records containing unexpected NULL values.

Click the Create Alert icon above the editor window and configure the alert criteria, such as the value column, the condition, and the threshold.

In the Notification section, select the webhook alert destination that was created earlier and click Create Alert. References: What are Databricks SQL alerts?, Monitor alerts, Monitoring Your Business with Alerts, Using Automation Runbook Webhooks To Alert on Databricks Status Updates.

A data lakehouse is a data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data12. One of the key features of a data lakehouse is that it supports ACID-compliant transactions, which means that it ensures data integrity, consistency, and isolation across concurrent read and write operations3. This feature results in improved data quality over a traditional data lake, which does not support transactions and may suffer from data corruption, duplication, or inconsistency due to concurrent or streaming data ingestion and processing . References: 1: What is a Data Lakehouse? - Databricks 2: What is a Data Lakehouse? Definition, features & benefits. - Qlik 3: ACID Transactions - Databricks : [Data Lake vs Data Warehouse: Key Differences] : [Data Lakehouse: The Future of Data Engineering]

 In a medallion architecture, a common data design pattern for lakehouses, data flows from Bronze to Silver to Gold layer tables, with each layer progressively improving the structure and quality of data. Bronze tables store raw data ingested from various sources, while Silver tables apply minimal transformations and cleansing to create an enterprise view of the data. Silver tables can also join and enrich data from different Bronze tables to provide a more complete and consistent view of the data. Therefore, option D is the correct answer, as Silver tables contain a more refined and cleaner view of data than Bronze tables. Option A is incorrect, as it is the opposite of the correct answer. Option B is incorrect, as Silver tables do not necessarily contain aggregates, but can also store detailed records. Option C is incorrect, as Silver tables may contain less data than Bronze tables, depending on the transformations and cleansing applied. Option E is incorrect, as Silver tables may contain more data than Bronze tables, depending on the joins and enrichments applied. References: What is a Medallion Architecture?, Transforming Bronze Tables in Silver Tables, What is the medallion lakehouse architecture?

https://spark.apache.org/docs/latest/api/python/reference/pyspark.ss/api/pyspark.sql.streaming.DataStreamWriter.trigger.html

 The VACUUM command is used to remove files that are no longer referenced by a Delta table and are older than the retention threshold1. The default retention period is 7 days2, but it can be changed by setting the delta.logRetentionDuration and delta.deletedFileRetentionDuration configurations3. If the VACUUM command was run on the table with a retention period shorter than 3 days, then the data files that were needed to restore the table to a 3-day-old version would have been deleted. The other commands do not delete data files from the table. The TIME TRAVEL command is used to query a historical version of the table4. The DELETE HISTORY command is not a valid command in Delta Lake. The OPTIMIZE command is used to improve the performance of the table by compacting small files into larger ones5. The HISTORY command is used to retrieve information about the operations performed on the table. References: 1: VACUUM | Databricks on AWS 2: Work with Delta Lake table history | Databricks on AWS 3: [Delta Lake configuration | Databricks on AWS] 4: Work with Delta Lake table history - Azure Databricks 5: [OPTIMIZE | Databricks on AWS] : [HISTORY | Databricks on AWS]

The reason why all of the data files and metadata files were deleted from the file system after dropping the table is that the table was managed. A managed table is a table that is created and managed by Spark SQL. It stores both the data and the metadata in the default location specified by the spark.sql.warehouse.dir configuration property. When a managed table is dropped, both the data and the metadata are deleted from the file system.

Option B is not correct, as the size of the table’s data does not affect the behavior of dropping the table. Whether the table’s data is smaller or larger than 10 GB, the data files and metadata files will be deleted if the table is managed, and will be preserved if the table is external.

Option C is not correct, for the same reason as option B.

Option D is not correct, as an external table is a table that is created and managed by the user. It stores the data in a user-specified location, and only stores the metadata in the Spark SQL catalog. When an external table is dropped, only the metadata is deleted from the catalog, but the data files are preserved in the file system.

Option E is not correct, as a table must have a location to store the data. If the location is not specified by the user, it will use the default location for managed tables. Therefore, a table without a location is a managed table, and dropping it will delete both the data and the metadata.

The best action that the data engineer can perform to improve the start up time for the clusters used for the Job is to use clusters that are from a cluster pool. A cluster pool is a set of idle clusters that can be used by jobs or interactive sessions. By using a cluster pool, the data engineer can avoid the cluster creation time and reduce the latency of the tasks. Cluster pools also offer cost savings and resource efficiency, as they can be shared by multiple users and jobs.

Option A is not relevant, as endpoints available in Databricks SQL are used for creating and managing SQL analytics workloads, not for improving cluster start up time.

Option B is not correct, as jobs clusters and all-purpose clusters have similar start up times. Jobs clusters are clusters that are dedicated to run a single job and are terminated when the job is completed. All-purpose clusters are clusters that can be used for multiple purposes, such as interactive sessions, notebooks, or multiple jobs. Both types of clusters can benefit from using a cluster pool.

Option C is not advisable, as configuring the clusters to be single-node will reduce the parallelism and performance of the tasks. Single-node clusters are clusters that have only one worker node and are typically used for testing or development purposes. They are not suitable for running production jobs that require high scalability and fault tolerance.

Option E is not helpful, as configuring the clusters to autoscale for larger data sizes will not affect the start up time of the clusters. Autoscaling is a feature that allows clusters to dynamically adjust the number of worker nodes based on the workload. It can help optimize the resource utilization and cost efficiency of the clusters, but it does not speed up the cluster creation process.

 In a medallion architecture, a common data design pattern for lakehouses, data flows from Bronze to Silver to Gold layer tables, with each layer progressively improving the structure and quality of data. Bronze tables store raw data ingested from various sources, while Silver tables apply minimal transformations and cleansing to create an enterprise view of the data. Silver tables can also join and enrich data from different Bronze tables to provide a more complete and consistent view of the data. Therefore, option D is the correct answer, as Silver tables contain a more refined and cleaner view of data than Bronze tables. Option A is incorrect, as it is the opposite of the correct answer. Option B is incorrect, as Silver tables do not necessarily contain aggregates, but can also store detailed records. Option C is incorrect, as Silver tables may contain less data than Bronze tables, depending on the transformations and cleansing applied. Option E is incorrect, as Silver tables may contain more data than Bronze tables, depending on the joins and enrichments applied. References: What is a Medallion Architecture?, Transforming Bronze Tables in Silver Tables, What is the medallion lakehouse architecture?

 An external table is a table that is defined in the metastore and points to an existing location in the storage system. When you drop an external table, only the metadata is deleted from the metastore, but the data files are not deleted from the storage system. This is because external tables are meant to be shared by multiple applications and users, and dropping them should not affect the data availability. On the other hand, a managed table is a table that is defined in the metastore and also managed by the metastore. When you drop a managed table, both the metadata and the data files are deleted from the metastore and the storage system, respectively. This is because managed tables are meant to be exclusive to the application or user that created them, and dropping them should free up the storage space. Therefore, the correct answer is C, because the table was external and only the metadata was deleted when the table was dropped. References: Databricks Documentation - Managed and External Tables, Databricks Documentation - Drop Table

To create a table in Databricks using data from an SQLite database, the correct syntax involves specifying the format of the data source. The format in the case of using JDBC (Java Database Connectivity) with SQLite is specified by the org.apache.spark.sql.jdbc format. This format allows Spark to interface with various relational databases through JDBC. Here is how the command should be structured:

CREATE TABLE jdbc_customer360

USING org.apache.spark.sql.jdbc

OPTIONS (

url "jdbc:sqlite:/customers.db",

dbtable "customer360"

)

The USING org.apache.spark.sql.jdbc line specifies that the JDBC data source is being used, enabling Spark to interact with the SQLite database via JDBC.

In Databricks, when creating a table, you can add a comment to columns or the entire table to provide more information about the data it contains. In this case, since it’s organization policy to indicate that the new table includes personally identifiable information (PII), option D is correct. The line of code would be added after defining the table structure and before closing with a semicolon. References: Data Engineer Associate Exam Guide, CREATE TABLE USING (Databricks SQL)

Option D is the correct answer because it enables the Serverless feature for the SQL endpoint, which allows the endpoint to automatically scale up and down based on the query load. This way, the endpoint can handle more concurrent queries and reduce the time it takes to return results. The Serverless feature also reduces the cold start time of the endpoint, which is the time it takes to start the cluster when a query is submitted to a non-running endpoint. The Serverless feature is available for both AWS and Azure Databricks platforms.

https://spark.apache.org/docs/latest/api/python/reference/pyspark.ss/api/pyspark.sql.streaming.DataStreamWriter.trigger.html

A data lakehouse is a data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data12. One of the key features of a data lakehouse is that it supports ACID-compliant transactions, which means that it ensures data integrity, consistency, and isolation across concurrent read and write operations3. This feature results in improved data quality over a traditional data lake, which does not support transactions and may suffer from data corruption, duplication, or inconsistency due to concurrent or streaming data ingestion and processing . References: 1: What is a Data Lakehouse? - Databricks 2: What is a Data Lakehouse? Definition, features & benefits. - Qlik 3: ACID Transactions - Databricks : [Data Lake vs Data Warehouse: Key Differences] : [Data Lakehouse: The Future of Data Engineering]

The STREAM function is used to process data from a streaming live table or view, which is a table or view that contains data that has been added only since the last pipeline update. Streaming live tables and views are stateful, meaning that they retain the state of the previous pipeline run and only process new data based on the current query. This is useful for incremental processing of streaming or batch data sources. The customers table in the query is a streaming live table, which means that it contains the latest data from the source. The STREAM function enables the query to read the data from the customers table incrementally and create another streaming live table named loyal_customers, which contains the customer IDs of the customers with high loyalty level. References: Difference between LIVE TABLE and STREAMING LIVE TABLE, CREATE STREAMING TABLE, Load data using streaming tables in Databricks SQL.

To sync a Databricks Repo with the changes from a central Git repository, the data engineer needs to run the Git pull operation. This operation fetches the latest updates from the remote repository and merges them with the local repository. The data engineer can use the Pull button in the Databricks Repos UI, or use the git pull command in a terminal session. The other options are not relevant for this task, as they either push changes to the remote repository (Push), combine two branches (Merge), save changes to the local repository (Commit), or create a new local repository from a remote one (Clone). References:

Run Git operations on Databricks Repos

Git pull

Delta Live Tables is a tool that enables data engineers to build and manage reliable data pipelines with minimal code. One of the features of Delta Live Tables is data quality monitoring, which allows data engineers to define quality expectations for their data and automatically check them at every step of the pipeline. Data quality monitoring can help detect and resolve data quality issues, such as missing values, duplicates, outliers, or schema changes. Data quality monitoring can also generate alerts and reports on the quality level of the data, and enable data engineers to troubleshoot and fix problems quickly. References: Delta Live Tables Overview, Data Quality Monitoring

Cron syntax is a tool that can be used to represent and submit a complex run schedule programmatically. Cron syntax is a string of six fields that specify the frequency, date, and time of a job run. For example, the cron expression 0 0 12 * * ? means run the job at 12:00 PM every day. The data engineer can use the Databricks REST API to create or update a job with a cron schedule. The data engineer can also use the Databricks CLI to create or update a job with a cron schedule by using a JSON file that contains the cron expression. The other tools are either invalid or not suitable for representing and submitting a complex run schedule programmatically. References: Schedule a job, Jobs API, Databricks CLI, Cron expressions

The processingTime option specifies a time-based trigger interval for fixed interval micro-batches. This means that the query will execute a micro-batch to process data every 5 seconds, regardless of how much data is available. This option is suitable for near-real time processing workloads that require low latency and consistent processing frequency. The other options are either invalid syntax (A, C), default behavior (B), or experimental feature (E). References: Databricks Documentation - Configure Structured Streaming trigger intervals, Databricks Documentation - Trigger.

The best practice is to use "Complete" as output mode instead of "append" when working with aggregated tables. Since gold layer is work final aggregated tables, the only option with output mode as complete is option E.

A Gold table is a table that contains highly refined and aggregated data that powers analytics, machine learning, and production applications. It represents data that has been transformed into knowledge, rather than just information. A Gold table is typically the final output of a medallion lakehouse architecture, where data flows from Bronze to Silver to Gold tables, with each layer improving the structure and quality of data. A job that queries aggregated data designed to feed into a dashboard is an example of a data workload that will utilize a Gold table as its source, as it requires data that is ready for consumption and analysis. The other options are either data workloads that will use a Bronze or Silver table as their source, or data workloads that will produce a Gold table as their output. References: Databricks Documentation - What is the medallion lakehouse architecture?, Databricks Documentation - What is a Medallion Architecture?, K21Academy - Delta Lake Architecture & Azure Databricks Workspace.

The best action that the data engineer can perform to improve the start up time for the clusters used for the Job is to use clusters that are from a cluster pool. A cluster pool is a set of idle clusters that can be used by jobs or interactive sessions. By using a cluster pool, the data engineer can avoid the cluster creation time and reduce the latency of the tasks. Cluster pools also offer cost savings and resource efficiency, as they can be shared by multiple users and jobs.

Option A is not relevant, as endpoints available in Databricks SQL are used for creating and managing SQL analytics workloads, not for improving cluster start up time.

Option B is not correct, as jobs clusters and all-purpose clusters have similar start up times. Jobs clusters are clusters that are dedicated to run a single job and are terminated when the job is completed. All-purpose clusters are clusters that can be used for multiple purposes, such as interactive sessions, notebooks, or multiple jobs. Both types of clusters can benefit from using a cluster pool.

Option C is not advisable, as configuring the clusters to be single-node will reduce the parallelism and performance of the tasks. Single-node clusters are clusters that have only one worker node and are typically used for testing or development purposes. They are not suitable for running production jobs that require high scalability and fault tolerance.

Option E is not helpful, as configuring the clusters to autoscale for larger data sizes will not affect the start up time of the clusters. Autoscaling is a feature that allows clusters to dynamically adjust the number of worker nodes based on the workload. It can help optimize the resource utilization and cost efficiency of the clusters, but it does not speed up the cluster creation process.

 Databricks cluster pools are a set of idle, ready-to-use instances that can reduce cluster start and auto-scaling times. This is useful for scenarios where a data team needs to run an automated report as quickly as possible, without waiting for the cluster to launch or scale up. Cluster pools can also help save costs by reusing idle instances across different clusters and avoiding DBU charges for idle instances in the pool. References: Best practices: pools | Databricks on AWS, Best practices: pools - Azure Databricks | Microsoft Learn, Best practices: pools | Databricks on Google Cloud